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Record W36400935 · doi:10.1093/pch/20.2.67

Point-of-care ultrasound: An emerging technology in Canadian paediatrics

2015· article· en· W36400935 on OpenAlexaffabout
Daniel Rosenfield, Charisse Kwan, Jason Fischer

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPoint of care ultrasoundMedicinePoint of carePediatricsMedical physicsUltrasoundIntensive care medicineMedical emergencyRadiologyNursing

Abstract

fetched live from OpenAlex

The use of point-of-care ultrasound (POCUS) in Canadian paediatric medicine has grown exponentially in the past decade. Early adopters of the technology in paediatrics include anesthesiology, critical care, emergency medicine and rheumatology (1,2). The emergence of this innovative technology across specialties has been driven by its ability to deliver affordable, real-time imaging of patient anatomy at the bedside without pain or radiation. The purpose of the present commentary is to describe the current clinical use of POCUS in Canadian paediatric medicine and to forecast its role in the near future. POCUS is defined as ultrasonography brought to the patient’s bedside and performed by a health care provider in conjunction with a clinical examination. Diagnostic POCUS is a problem-based assessment that is generally qualitative and binary (or semiquantitative). The dynamic, real-time findings are correlated directly with the patient’s presenting signs and symptoms, and scans can be repeated in a serial fashion. This focused approach enables the provider to maintain workflow while gathering key pieces of information that can narrow or determine diagnosis, streamline care, guide ongoing management and reduce cognitive errors. For example, an emergency medicine provider may perform a POCUS examination of a child’s abdomen with a high pretest probability of intussusception based on history and physical examination. The focused objective of the scan is to identify the presence or absence of a ‘target sign’. In contrast, a radiology-performed, comprehensive ultrasound of the child’s abdomen would describe the entire anatomy of the abdomen, including a systematic, detailed description of the solid and hollow viscous organs. The ability of POCUS to visualize anatomy in real-time has led to its widespread use in peripheral venous access, regional anesthesia, foreign-body removal, fluid aspiration and fracture relocation confirmation, as well as several life-saving procedures such as pericardiocentesis and confirmation of endotracheal intubation (2,3). Its role in the assessment of the adult trauma patient has been well established and the extended focused sonography for trauma is rapidly becoming the standard of care in paediatric trauma (4). The body of literature supporting the patient benefits of these diagnostic and procedural POCUS applications continues to rapidly expand as the capacity for research expands. This includes applications specific to paediatrics such as examining for intussusception (5), skull fracture (6), lung pathology (7,8), soft-tissue infection (9) and appendicitis (10). The current literature demonstrates that novice users can be trained to use the technology in a competency-based manner that is specialty-specific and feasible in both duration and resource use. The implementation and capacity building of POCUS across specialties is now underway, with most Canadian paediatric institutions having overcome the challenges of bureaucratic inertia, resource constraint and lack of expertise. In the United States, adoption has been more rapid (11). The percentage of United States emergency departments with paediatric emergency medicine (PEM) training programs using POCUS has climbed from 65% in 2006 to 95% in 2011, with 88% of these incorporating POCUS into their PEM Fellowship curricula (12). In Canada, it is anticipated that the Royal College of Physicians and Surgeons of Canada will begin to incorporate mandatory training in POCUS into paediatric emergency fellowship training curricula to match current evidence (G Neto, University of Ottawa [Ottawa, Ontario], personal communication). This has led to a demand for leaders in POCUS and the emergence of POCUS-specific fellowships that provide physicians with the administrative, research and scholarly skills needed to create and lead POCUS programs. In addition, mid-career physicians seeking training in POCUS are now being offered greater opportunities through workshops, immersive trainerships and reverse mentoring from their trainees (11). The recognition of ultrasound as a core clinical skill across specialties has led to its recent incorporation into undergraduate medical education. Examples include the longitudinal curricula that have been introduced at the McGill University (Montreal, Quebec) and University of Toronto (Toronto, Ontario) Schools of Medicine. Although still in development, these programs will encompass all four years of training with a goal of achieving basic diagnostic and procedural competency before residency (13) (I Devito, University of Toronto, personal communication). The expertise of POCUS users from various specialties has made these undergraduate programs educationally rich and incredibly popular among students (14). The continued widespread adoption of POCUS in paediatric medicine is predictable. Although being spearheaded by paediatric emergency departments, the decreasing cost and increasing personalization of ultrasound technology, in combination with its early introduction to medical students and a more connected world, allows for unprecedented self-learning and reverse innovation among all paediatric providers worldwide. Canadian providers must ensure that their current high standards of care are maintained and should be encouraged to seek opportunities for POCUS to improve their patient care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.348
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2015
Admission routes2
Has abstractyes

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